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author:

Hong, C. (Hong, C..) [1] | Wen, B. (Wen, B..) [2] | Lin, W. (Lin, W..) [3]

Indexed by:

Scopus PKU CSCD

Abstract:

The basic concept of RBF(Radial Basis Function) ANN model based on OLS(Orthogonal Least Squares) method is presented and the sensitivity of the traditional OLS-RBF model to the initial width of basic function is analyzed. The gradient descent method is applied to adjust and decide the initial width of basic function, which effectively reduces its impact on network. With the wind speed and environment temperature as inputs, the power output of a coastal wind farm in Fujian is forecasted by the improved model and traditional model respectively, which shows that, the improved model has better precision and accuracy.

Keyword:

Forecasting; Gradient descent method; Improved OLS-RBF ANN; Models; Neural networks; Short-term wind power forecasting; Wind power

Community:

  • [ 1 ] [Hong, C.]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
  • [ 2 ] [Wen, B.]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
  • [ 3 ] [Lin, W.]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China

Reprint 's Address:

  • [Hong, C.]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China

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Source :

Electric Power Automation Equipment

ISSN: 1006-6047

Year: 2012

Issue: 9

Volume: 32

Page: 40-43,59

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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